agent-to-agent
Skill378 starsChanged 52 days agoMITView on GitHub
Agent-to-Agent (A2A) communication protocol. Connect two or more Claude agents that pass messages, share context, delegate tasks, and collaborate. Implements structured handoffs, shared memory, and multi-agent conversations.
In this repository229 other files it gives its agents.
- accessibility-auditorSkill
- agent-armySkill
- agent-eval-harnessSkill
- agent-swarm-deployerSkill
- agent-team-builderSkill
- ai-coding-guardrailsSkill
- ai-readiness-assessmentSkill
- animateSkill
- api-documentation-writerSkill
- api-endpoint-scaffolderSkill
- api-load-testerSkill
- athlete-social-media-managerSkill
- before-you-buildSkill
- bi-measure-builderSkill
- board-deck-generatorSkill
- bookkeeping-closeSkill
- bracket-predictorSkill
- brand-consistency-checkerSkill
- brand-voice-analyzerSkill
- browser-test-loopSkill
- budget-optimizerSkill
- cash-flow-forecasterSkill
- champion-identifierSkill
- churn-autopsySkill
- claude-design-criticSkill
- claude-design-system-architectSkill
- claude-landing-composerSkill
- claude-md-writerSkill
- client-health-dashboardSkill
- client-proposal-generatorSkill
- code-review-proSkill
- cold-email-sequence-generatorSkill
- color-palette-extractorSkill
- commercial-lease-analyzerSkill
- company-announcement-writerSkill
- competitor-content-analyzerSkill
- competitor-intel-agentSkill
- competitor-price-trackerSkill
- compliance-checkerSkill
- contact-hunterSkill
- content-repurposerSkill
- context-budgetSkill
- contract-analyzerSkill
- contract-redlinerSkill
- conversation-archaeologistSkill
- cowork-calendar-defragSkill
- cowork-contract-renewal-radarSkill
- cowork-data-room-builderSkill
- cowork-deal-roomSkill
- cowork-expense-auditSkill
- cowork-folder-organizerSkill
- cowork-hiring-screenerSkill
- cowork-home-inventorySkill
- cowork-inbox-triageSkill
- cowork-invoice-chaserSkill
- cowork-medical-bill-auditorSkill
- cowork-qbr-builderSkill
- cowork-rfp-responseSkill
- cowork-sop-writerSkill
- cowork-tax-prep-organizerSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 0 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈0
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 35
Treat .a2a-context.json as the single source of truth.
- Very specific: line 38
Never write secrets to .a2a-context.json.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent-to-Agent (A2A) Communication Protocol
Act as the A2A Coordinator: a protocol layer that lets multiple Claude Code agents communicate, collaborate, and delegate work through structured message passing, shared context, and formal handoffs. Orchestrate every interaction through the shared context file .a2a-context.json and the Agent tool.
Contents
references/protocol.md— message format, message types, lifecycle, shared context schema, atomic read-modify-write, context size management.references/registry.md— agent registration, capability discovery, built-in agent templates.references/patterns.md— request/response, pipeline, fan-out/fan-in, conversation, supervisor.references/handoff.md— structured handoff, acceptance, rejection, chain tracking.references/error-handling.md— timeouts, rejections, deadlock detection, degradation, escalation matrix.references/workflows.md— worked examples (research+writer, code+review, sales+technical).references/operations.md— coordination commands, best practices, monitoring, security, init detail.
Workflow
- Understand the goal. Determine what the user wants to accomplish with multiple agents.
- Design the team. Decide which agents are needed; draw from the templates in
references/registry.mdor write custom specs. - Choose the pattern. Select pipeline, fan-out/fan-in, conversation, or supervisor from
references/patterns.md. Prefer pipeline when order matters, fan-out when subtasks are independent. - Initialize. Locate the project root. Read
.a2a-context.jsonif it exists and report current state; otherwise create it from the template inreferences/operations.md. Register every agent into theagentssection perreferences/registry.md. - Execute. Dispatch agents via the Agent tool following the chosen pattern. Structure each agent prompt with identity, context, task, output location, protocol, and constraints (see
references/operations.md). For parallelism, issue multiple Agent tool calls in a single response. - Coordinate handoffs. When an agent transfers a task, require a full handoff payload and an ACK, and append to the task
chain. Followreferences/handoff.md. - Monitor and recover. Read
.a2a-context.jsonto track progress. On timeout, rejection, deadlock, or failure, apply the procedures and escalation matrix inreferences/error-handling.md. Cap retries at 3 before escalating to the user. - Deliver. Merge all agent findings into the
conclusionssection and present the final output.
Core Rules
- Treat
.a2a-context.jsonas the single source of truth. Read it before acting; write the complete file back after modifying. Follow the atomic read-modify-write procedure inreferences/protocol.md. - Conform every inter-agent message to the schema in
references/protocol.md. - Confine each agent to writing its own section plus shared
conclusions. Restrict task assignment changes to the Coordinator. - Never write secrets to
.a2a-context.json; pass sensitive data in-memory through Agent prompts and add the file to.gitignore. - Require explicit ERROR messages for all failures; never fail silently. Run context summarization when the file exceeds 50KB.
Minimal Example: 2-Agent Pipeline
User: "Research the top 5 AI frameworks and write a comparison article."
Coordinator:
1. Create .a2a-context.json
2. Register: researcher, writer
3. Dispatch researcher: "Search for top 5 AI frameworks, compare features, performance, ecosystem"
4. Read researcher's findings from shared context
5. Dispatch writer: "Using the research findings, write a 1200-word comparison article"
6. Read writer's draft from shared context
7. Present the final article to the userDoes it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

